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Support Vector Machine for Handwritten Devanagari Numeral Recognition
"... Support Vector Machines (SVM) is used for classification in pattern recognition widely. This paper applies this technique for recognizing handwritten numerals of Devanagari Script. Since benchmark database does not exist globally, this system is constructed database by implementing Automated Numeral ..."
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Support Vector Machines (SVM) is used for classification in pattern recognition widely. This paper applies this technique for recognizing handwritten numerals of Devanagari Script. Since benchmark database does not exist globally, this system is constructed database by implementing Automated Numeral Extraction and Segmentation Program (ANESP). Preprocessing is manifested in the same program which reduces most of the efforts. 2000 samples are collected from 20 different people having variation in writing style. Moment Invariant and Affine Moment Invariant techniques are used as feature extractor. These techniques extract 18 features from each image which is used in Support Vector Machine for recognition purpose. Binary classification techniques of Support Vector Machine is implemented and linear kernel function is used in SVM. This linear SVM produces 99.48 % overall recognition rate which is the highest among all techniques applied on handwritten Devanagari numeral recognition system.
Online Handwriting Recognition of Hindi Numerals using
"... Handwriting recognition has attracted many researchers across the world. Recognition of online handwritten Hindi numerals is a goal of many research efforts in the pattern recognition field. This paper presents an online handwritten Hindi numeral recognition system using Support Vector Machines. Co- ..."
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Handwriting recognition has attracted many researchers across the world. Recognition of online handwritten Hindi numerals is a goal of many research efforts in the pattern recognition field. This paper presents an online handwritten Hindi numeral recognition system using Support Vector Machines. Co-ordinate points of the input handwritten numeral are collected; various algorithms for pre-processing are applied for normalizing, resampling and interpolating missing points. Angle, curvature along with the x and y coordinates are extracted from the input handwritten numeral. The data obtained is then used for recognition using the kernel functions of SVM. The recognition accuracies are obtained on different schemes of data using the four kernel functions of SVM.
Devnagari Handwriting Recognition using STANN
"... Several approaches to recognize the handwritten characters and numerals such as online cursive handwriting and numerals recognition have been proposed. Most of them are based on neural network approaches. In this paper, a technique for continuous recognition of handwritten Devanagari characters is p ..."
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Several approaches to recognize the handwritten characters and numerals such as online cursive handwriting and numerals recognition have been proposed. Most of them are based on neural network approaches. In this paper, a technique for continuous recognition of handwritten Devanagari characters is proposed. This approach employs a method based on Spatio-Temporal Artificial Neural Network (STANN). The proposed method is efficient in the field of online handwriting recognition because of the property of STANN to detection of spikes of the continuous input signals. The method is based on different steps of recognition. The results of signals are extracted from the input handwritten characters where the spikes signals are generated by the continuous signals of the input character. A new algorithm using STANN is proposed with results in this paper. General Terms Devnagari handwriting recognition, online character recognition
